So called image recognition technologies have been developed for various applications including traffic safety, aerospace positioning, etc. Some of such technologies are briefly described in the following:
Feature Extraction and Image Recognition
One of major methods to handle imagery data is a feature based method in which specific subsets representing particular characteristics are sought in an image and tracked over multiple images. Feature extraction will look for mathematical characteristics representing a distinguishable special feature, e.g., an edge or a corner, while image recognition will look for an object, e.g., a traffic sign, by correlating a subset with a prepared object template.
Feature Extraction And Positioning in Aerospace Application
Feature extraction approach is commonly employed in aerospace application in the way of integrating miscellaneous feature positioning and navigation for navigation accuracy enhancement. This technical area has been actively studied since first application took place in lunar landing mission of the Apollo project in 1968 to the recent application in aerial reconnaissance. The most noticeable development in modern history common in numerous aerospace applications is deemed SLAM, or Simultaneous Localization And Mapping, originally developed in 1986 for robotics. The original concept of SLAM was aimed at dead reckoning with the following functions: (1) extraction of miscellaneous imagery features; (2) estimation of extracted feature positions; (3) estimation of camera position and orientation; and (4) registration of miscellaneous feature positions in the database for the re-calibration purpose upon next arrival.
Image Recognition in Automotive Application
Meanwhile, automotive application of visual cues has been developed primarily in image recognition to detect particular objects beneficial to drivers, such as, highway lanes, traffic signs, and pedestrians. This is mainly because visual cues have been more appreciated for human interface rather than navigation accuracy enhancement in automotive application.
Feature Positioning in DARPA GRAND CHALLENGE
Automotive application of integrated feature positioning and navigation systems has called rapid public interests upon recent DARPA Grand Challenge. In this open public race, autonomously guided uninhabited vehicles (i.e., robots) use SLAM technology just like aerospace application to dodge obstacles and make the right paths with measurements from Light Detection and Ranging (LIDAR) sensors, radars, cameras, inertial sensors, and GPS receivers. In this scenario, unlike conventional automotive application, visual cues are used mainly for navigation aid but not for image recognition disregarding human interface because of “uninhabited” and “precision” nature of the mission.
Image Recognition and Feature Positioning in Future
Learning from this very-high-end application, automotive industry is now seeking for low-cost solutions to use visual cues not only for image recognition but also for feature extraction and possibly positioning to achieve extended performance in navigation and drive assist, such as, lane guidance, safety-level navigation accuracy, driver's motion intent inference, etc.
Therefore, there is a need of a new architecture to use visual cues not only for object recognition but also for feature extraction and positioning in automotive application with low-cost sensor configurations for navigation accuracy enhancement and extended drive assist applications.